code: def node_classify_model(config):
      """Build node classify model."""
      nodes = fl.data("nodes", shape=[None, 1], dtype="int64")
      labels = fl.data("labels", shape=[None, 1], dtype="int64")
  
      embed_nodes = fl.embedding(
          input=nodes,
          size=[config.num_nodes, config.embed_dim],
          param_attr=fluid.ParamAttr(name="weight"),
      )
  
      embed_nodes.stop_gradient = True
      probs = fl.fc(
          input=embed_nodes, size=config.num_labels, act="softmax"
      )
      predict = fl.argmax(probs, axis=-1)
      loss = fl.cross_entropy(input=probs, label=labels)
      loss = fl.reduce_mean(loss)
  
      return {
          "loss": loss,
          "probs": probs,
          "predict": predict,
          "labels": labels,
      }
  
file_path: PGL/legacy/examples/distribute_metapath2vec/multi_class.py
imports: import argparse
  import sys
  import os
  import tqdm
  import time
  import math
  import logging
  import random
  import pickle
  import numpy
  import sklearn.metrics
  from sklearn.metrics import f1_score
  import pgl
  import paddle.fluid
  import paddle.fluid.layers
label_desc: Use image node_id 2 for calling a global function with example usage: node_classify_model(config) and returns: dict